# !pip install scikit-learn
# pip install xgboost
# pip install --upgrade scikit-learn xgboost
# pip install statsmodels
# pip install mplcursors
# !pip install folium

import warnings  # To handle and suppress warnings
warnings.filterwarnings("ignore")  # Ignore warnings to keep the output clean

import os      # To interact with the operating system (e.g., file paths)
import numpy as np     # For numerical operations and array handling
import pandas as pd     # For data manipulation and analysis
import seaborn as sns     # For advanced data visualization (based on Matplotlib)
import xgboost as xgb       # For XGBoost machine learning models (gradient boosting)
import matplotlib.pyplot as plt     # For basic plotting and data visualization
import mplcursors    #to hover the cursor
import folium     # to plot the map visuals

from sklearn.model_selection import KFold        # For splitting data into k-folds for cross-validation
from sklearn.model_selection import train_test_split      # For splitting data into training and testing sets
from sklearn.metrics import mean_absolute_percentage_error      # To calculate error metric for regression tasks
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor        # For ensemble regression models
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, TimeSeriesSplit        # For hyperparameter tuning and cross-validation

from xgboost import plot_importance, plot_tree       # To visualize XGBoost model importance and decision trees
from statsmodels.tsa.seasonal import seasonal_decompose      # For time series seasonal decomposition

Uber_Jan_Feb_FOIL = pd.read_csv("Uber-Jan-Feb-FOIL.csv")
Uber_Jan_Feb_FOIL

other_American_B01362 = pd.read_csv("other-American_B01362.csv")
other_American_B01362

#in this file if we don't used encoding ="ISO-8859-1" it throws UnicodeDecodeError
other_Carmel_B00256 = pd.read_csv("other-Carmel_B00256.csv", encoding='ISO-8859-1')
other_Carmel_B00256

other_Dial7_B00887 = pd.read_csv("other-Dial7_B00887.csv")
other_Dial7_B00887

other_Diplo_B01196 = pd.read_csv("other-Diplo_B01196.csv",encoding='ISO-8859-1')
other_Diplo_B01196

other_FHV_services_jan_aug_2015 = pd.read_csv("other-FHV-services_jan-aug-2015.csv")
other_FHV_services_jan_aug_2015

other_Federal_02216 = pd.read_csv("other-Federal_02216.csv")
other_Federal_02216

other_Firstclass_B01536 = pd.read_csv("other-Firstclass_B01536.csv",encoding = 'ISO-8859-1')
other_Firstclass_B01536

other_Highclass_B01717 = pd.read_csv("other-Highclass_B01717.csv",encoding = 'ISO-8859-1')
other_Highclass_B01717

other_Lyft_B02510 = pd.read_csv("other-Lyft_B02510.csv")
other_Lyft_B02510

other_Prestige_B01338 = pd.read_csv("other-Prestige_B01338.csv",encoding ="ISO-8859-1")
other_Prestige_B01338

other_Skyline_B00111 = pd.read_csv("other-Skyline_B00111.csv")
other_Skyline_B00111

uber_raw_data_apr14 = pd.read_csv("uber-raw-data-apr14.csv")
uber_raw_data_apr14

uber_raw_data_aug14 = pd.read_csv("uber-raw-data-aug14.csv")
uber_raw_data_aug14

uber_raw_data_janjune_15 = pd.read_csv("uber-raw-data-janjune-15.csv")
uber_raw_data_janjune_15

uber_raw_data_jul14 = pd.read_csv("uber-raw-data-jul14.csv")
uber_raw_data_jul14

uber_raw_data_jun14 = pd.read_csv("uber-raw-data-jun14.csv")
uber_raw_data_jun14

uber_raw_data_may14 = pd.read_csv("uber-raw-data-may14.csv")
uber_raw_data_may14

uber_raw_data_sep14 = pd.read_csv("uber-raw-data-sep14.csv")
uber_raw_data_sep14

uber_raw_data_janjune_15.shape  #(rows,column)

(14270479, 4)

uber_raw_data_janjune_15.info   # it get all information of table.

<bound method DataFrame.info of          Dispatching_base_num          Pickup_date Affiliated_base_num  \
0                      B02617  2015-05-17 09:47:00              B02617   
1                      B02617  2015-05-17 09:47:00              B02617   
2                      B02617  2015-05-17 09:47:00              B02617   
3                      B02617  2015-05-17 09:47:00              B02774   
4                      B02617  2015-05-17 09:47:00              B02617   
...                       ...                  ...                 ...   
14270474               B02765  2015-05-08 15:43:00              B02765   
14270475               B02765  2015-05-08 15:43:00              B02765   
14270476               B02765  2015-05-08 15:43:00              B02765   
14270477               B02765  2015-05-08 15:44:00              B01899   
14270478               B02765  2015-05-08 15:44:00              B02682   

          locationID  
0                141  
1                 65  
2                100  
3                 80  
4                 90  
...              ...  
14270474         186  
14270475         263  
14270476          90  
14270477          45  
14270478         144  

[14270479 rows x 4 columns]>

type(uber_raw_data_janjune_15) # type of table

pandas.core.frame.DataFrame

uber_raw_data_janjune_15.columns # it get all column names present in the table

Index(['Dispatching_base_num', 'Pickup_date', 'Affiliated_base_num',
       'locationID'],
      dtype='object')

uber_raw_data_janjune_15.head(10)  # it get first 10 rows of table

uber_raw_data_janjune_15.tail(10)  # it get the 10 rows from the bottom

uber_raw_data_janjune_15.index

RangeIndex(start=0, stop=14270479, step=1)

uber_raw_data_janjune_15.dtypes  # it get the data types of the table columns

Dispatching_base_num    object
Pickup_date             object
Affiliated_base_num     object
locationID               int64
dtype: object

uber_raw_data_janjune_15.describe() # it describes all the statistics operations

uber_raw_data_janjune_15.isnull().sum()  # it get the null values of table

Dispatching_base_num         0
Pickup_date                  0
Affiliated_base_num     162195
locationID                   0
dtype: int64

Uber_Jan_Feb_FOIL.columns

Index(['dispatching_base_number', 'date', 'active_vehicles', 'trips'], dtype='object')

Uber_Jan_Feb_FOIL.head()  # by default it get the first 5 rows

Uber_Jan_Feb_FOIL.isnull().sum()

dispatching_base_number    0
date                       0
active_vehicles            0
trips                      0
dtype: int64

Uber_Jan_Feb_FOIL.duplicated(subset=['dispatching_base_number']).sum() # it shows how much column has duplicted rows

np.int64(348)

Uber_Jan_Feb_FOIL.dropna(subset = 'dispatching_base_number',inplace = True) # it is used to drop the null value rows

Uber_Jan_Feb_FOIL.shape # it defines how many rows and columns a table has

(354, 4)

Uber_Jan_Feb_FOIL['date'] = pd.to_datetime(Uber_Jan_Feb_FOIL["date"]) # conversion of datetime datatype

# extraction of year, monthname, day, and hour

Uber_Jan_Feb_FOIL['year'] = Uber_Jan_Feb_FOIL['date'].dt.year
Uber_Jan_Feb_FOIL['month'] = Uber_Jan_Feb_FOIL['date'].dt.month_name()
Uber_Jan_Feb_FOIL['day'] = Uber_Jan_Feb_FOIL['date'].dt.day_name()
Uber_Jan_Feb_FOIL['hours'] = Uber_Jan_Feb_FOIL['date'].dt.hour

Uber_Jan_Feb_FOIL.head()

Uber_Jan_Feb_FOIL.dtypes

dispatching_base_number            object
date                       datetime64[ns]
active_vehicles                     int64
trips                               int64
year                                int32
month                              object
day                                object
hours                               int32
dtype: object

other_American_B01362['DATE'] = pd.to_datetime(other_American_B01362["DATE"])

other_American_B01362['year'] = other_American_B01362['DATE'].dt.year
other_American_B01362['month'] = other_American_B01362['DATE'].dt.month_name()
other_American_B01362['day'] = other_American_B01362['DATE'].dt.day_name()

other_American_B01362.head()

other_American_B01362['day'].value_counts()  # it counts values in particular column uniquly

day
Saturday     16552
Sunday       14511
Friday       14366
Tuesday      12011
Thursday     11844
Wednesday    11373
Monday       11055
Name: count, dtype: int64

other_American_B01362.shape

(91712, 9)

other_American_B01362.isnull().sum()

DATE                   0
TIME                   0
PICK UP ADDRESS        0
Unnamed: 3         91712
Unnamed: 4         91712
Unnamed: 5         91712
year                   0
month                  0
day                    0
dtype: int64

other_American_B01362["PICK UP ADDRESS"].value_counts()

PICK UP ADDRESS
 400 Brook Ave Mott Haven, BX                       1216
 331 E 132nd St Mott Haven, BX                       888
 545 E 145th St Mott Haven, BX                       708
 436 E 149th St South Bronx, BX                      660
 169 Cypress Ave Mott Haven, BX                      643
                                                    ... 
 10 Ed Koch Queensboro Bridge Queens                   1
BARRETTO POIN PARK   Barretto St Hunts Point, BX       1
 744 Saint Anns Ave South Bronx, BX                    1
 448 E 149th St South Bronx, BX                        1
 542 Southern Blvd South Bronx, BX                     1
Name: count, Length: 10608, dtype: int64

other_American_B01362.dropna()

other_American_B01362.shape

(91712, 9)

other_American_B01362.dtypes

DATE               datetime64[ns]
TIME                       object
PICK UP ADDRESS            object
Unnamed: 3                float64
Unnamed: 4                float64
Unnamed: 5                float64
year                        int32
month                      object
day                        object
dtype: object

other_Carmel_B00256.head()

other_Carmel_B00256.shape

(256519, 4)

other_Carmel_B00256.dropna()

other_Carmel_B00256.columns

Index(['Date', 'Time', 'PU_Adress', 'Base_No'], dtype='object')

other_Carmel_B00256['Date'] = pd.to_datetime(other_Carmel_B00256['Date'])

other_Carmel_B00256.dtypes

Date         datetime64[ns]
Time                 object
PU_Adress            object
Base_No              object
dtype: object

other_Dial7_B00887.head()

other_Dial7_B00887['State'].value_counts()

State
NY                                                                                                                               133408
NJ                                                                                                                                 6624
JFK JET BLUE DOMESTIC PICK UP AREA 3                                                                                               4072
EWR UNITED PICK-UP AREA 3                                                                                                          3319
LGA DELTA Pick-up Island                                                                                                           3231
                                                                                                                                  ...  
LGA DELTA CONNECTION MIDDLE P/U TERM D                                                                                                1
LGA DELTA DEPARTUES                                                                                                                   1
EWR SOUTHWEST DOOR 2                                                                                                                  1
JFK Air China Outside at the curb                                                                                                     1
EWR QANTAS Outside Door #6                                                                                                            1
Name: count, Length: 1001, dtype: int64

other_Dial7_B00887.shape

(194992, 6)

other_Dial7_B00887.dropna(inplace = True)

other_Dial7_B00887.dtypes

Date       object
Time       object
State      object
PuFrom     object
Address    object
Street     object
dtype: object

other_Dial7_B00887['Address'].value_counts()

Address
          17370
200        2173
1          2018
300        1513
2          1397
          ...  
15422         1
5040          1
6950          1
1971          1
4909          1
Name: count, Length: 4328, dtype: int64

other_Dial7_B00887['Date'] = pd.to_datetime(other_Dial7_B00887['Date'])

other_Dial7_B00887.dtypes

Date       datetime64[ns]
Time               object
State              object
PuFrom             object
Address            object
Street             object
dtype: object

other_Dial7_B00887.shape

(157962, 6)

other_Dial7_B00887.isnull().sum()

Date       0
Time       0
State      0
PuFrom     0
Address    0
Street     0
dtype: int64

other_Diplo_B01196.head()

other_Diplo_B01196.shape

(98550, 3)

other_Diplo_B01196.isnull().sum()

Date          0
Time          0
PU_Address    0
dtype: int64

other_Diplo_B01196.dropna()

other_Diplo_B01196['Date'] = pd.to_datetime(other_Diplo_B01196['Date'])

other_Diplo_B01196.dtypes

Date          datetime64[ns]
Time                  object
PU_Address            object
dtype: object

uber_raw_data_janjune_15.head()

uber_raw_data_janjune_15['Pickup_date'] = pd.to_datetime(uber_raw_data_janjune_15['Pickup_date'])

uber_raw_data_janjune_15.shape

(14270479, 4)

uber_raw_data_janjune_15.isnull().sum()

Dispatching_base_num         0
Pickup_date                  0
Affiliated_base_num     162195
locationID                   0
dtype: int64

uber_raw_data_janjune_15.dropna(inplace =True)

uber_raw_data_janjune_15.shape

(14108284, 4)

uber_raw_data_janjune_15['year'] = uber_raw_data_janjune_15['Pickup_date'].dt.year
uber_raw_data_janjune_15['month'] = uber_raw_data_janjune_15['Pickup_date'].dt.month_name()
uber_raw_data_janjune_15['day'] = uber_raw_data_janjune_15['Pickup_date'].dt.day_name()
uber_raw_data_janjune_15['hours'] = uber_raw_data_janjune_15['Pickup_date'].dt.hour

uber_raw_data_janjune_15.dtypes

Dispatching_base_num            object
Pickup_date             datetime64[ns]
Affiliated_base_num             object
locationID                       int64
year                             int32
month                           object
day                             object
hours                            int32
dtype: object

uber_raw_data_janjune_15.duplicated().sum()

np.int64(896732)

uber_raw_data_janjune_15.drop_duplicates(inplace = True)

uber_raw_data_janjune_15.duplicated().sum()

np.int64(0)

uber_raw_data_janjune_15.columns

Index(['Dispatching_base_num', 'Pickup_date', 'Affiliated_base_num',
       'locationID', 'year', 'month', 'day', 'hours'],
      dtype='object')

uber_raw_data_janjune_15.head()

uber_raw_data_janjune_15['locationID'].value_counts()

locationID
161    394018
231    374953
234    367047
79     356238
249    289464
        ...  
105        12
99          8
2           8
264         6
110         2
Name: count, Length: 262, dtype: int64

other_FHV_services_jan_aug_2015['Pick Up Date'] = pd.to_datetime(other_FHV_services_jan_aug_2015['Pick Up Date'])

other_FHV_services_jan_aug_2015.dtypes

Base Number                   object
Base Name                     object
Pick Up Date          datetime64[ns]
Number of Trips               object
Number of Vehicles            object
dtype: object

# Convert to string, replace '-', and clean up commas and spaces
other_FHV_services_jan_aug_2015['Number of Trips'] = (
    other_FHV_services_jan_aug_2015['Number of Trips']
    .astype(str)  # Ensure all values are strings
    .str.replace('-', '0')  # Replace dash with 0
    .str.replace(',', '')  # Remove commas
    .str.strip()  # Remove extra spaces
)
other_FHV_services_jan_aug_2015['Number of Trips'] = pd.to_numeric(other_FHV_services_jan_aug_2015['Number of Trips'])

# Repeat same for 'Number of Vehicles'
other_FHV_services_jan_aug_2015['Number of Vehicles'] = (
    other_FHV_services_jan_aug_2015['Number of Vehicles']
    .astype(str)
    .str.replace('-', '0')
    .str.replace(',', '')
    .str.strip()
)
other_FHV_services_jan_aug_2015['Number of Vehicles'] = pd.to_numeric(other_FHV_services_jan_aug_2015['Number of Vehicles'])

other_FHV_services_jan_aug_2015['Number of Vehicles'].value_counts()

Number of Vehicles
0      1716
1      1571
2      1178
3      1119
5      1011
       ... 
531       1
277       1
267       1
633       1
221       1
Name: count, Length: 445, dtype: int64

other_FHV_services_jan_aug_2015['Number of Trips'].value_counts()

Number of Trips
1       988
2       714
4       611
6       542
3       523
       ... 
1792      1
2230      1
3352      1
1270      1
3277      1
Name: count, Length: 1906, dtype: int64

other_FHV_services_jan_aug_2015.dtypes

Base Number                   object
Base Name                     object
Pick Up Date          datetime64[ns]
Number of Trips                int64
Number of Vehicles             int64
dtype: object

other_FHV_services_jan_aug_2015.isnull().sum()

Base Number           0
Base Name             0
Pick Up Date          0
Number of Trips       0
Number of Vehicles    0
dtype: int64

other_FHV_services_jan_aug_2015.columns

Index(['Base Number', 'Base Name', 'Pick Up Date', 'Number of Trips',
       'Number of Vehicles'],
      dtype='object')

other_FHV_services_jan_aug_2015.head()

total_trips_per_base = other_FHV_services_jan_aug_2015.groupby('Base Name')['Number of Trips'].sum().reset_index()

# Renaming the columns for my clarity
total_trips_per_base.rename(columns={'Number of Trips': 'Total Number of Trips'}, inplace=True)

total_trips_per_base

other_FHV_services_jan_aug_2015['Year-Month'] = other_FHV_services_jan_aug_2015['Pick Up Date'].dt.to_period('M')

monthly_trend = other_FHV_services_jan_aug_2015.groupby('Year-Month')['Number of Trips'].sum().reset_index()

# Renaming columns for clarity
monthly_trend.rename(columns={'Number of Trips': 'Total Monthly Trips'}, inplace=True)

monthly_trend

other_FHV_services_jan_aug_2015 = other_FHV_services_jan_aug_2015.dropna(subset=['Number of Trips','Number of Vehicles'])

total_trips = other_FHV_services_jan_aug_2015['Number of Trips'].sum()
total_vehicles = other_FHV_services_jan_aug_2015['Number of Vehicles'].sum()

average_trips_per_vehicle = total_trips / total_vehicles

print(f"Total Trips: {total_trips}")
print(f"Total Vehicles: {total_vehicles}")
print(f"Average Trips per Vehicle: {average_trips_per_vehicle:.2f}")

Total Trips: 6550836
Total Vehicles: 1039319
Average Trips per Vehicle: 6.30

other_FHV_services_jan_aug_2015 = other_FHV_services_jan_aug_2015.dropna(subset=['Pick Up Date'])

other_FHV_services_jan_aug_2015['Month'] = other_FHV_services_jan_aug_2015['Pick Up Date'].dt.to_period('M')
monthly_trend = other_FHV_services_jan_aug_2015.groupby(['Base Name', 'Month'])['Number of Trips'].sum().reset_index()

monthly_trend_sorted = monthly_trend.sort_values(by='Number of Trips', ascending=False)

monthly_trend_sorted

other_Federal_02216['Date'] = pd.to_datetime(other_Federal_02216['Date'])

other_Federal_02216.head()

other_Federal_02216.dtypes

Date               datetime64[ns]
Time                       object
PU_Address                 object
DO_Address                 object
Routing Details            object
PU_Address.1               object
Status                     object
dtype: object

other_Federal_02216.shape

(267, 7)

other_Federal_02216.dropna(inplace =True)

other_Federal_02216.isnull().sum()

Date               0
Time               0
PU_Address         0
DO_Address         0
Routing Details    0
PU_Address.1       0
Status             0
dtype: int64

other_Firstclass_B01536['DATE'] = pd.to_datetime(other_Firstclass_B01536['DATE'])

other_Firstclass_B01536.dtypes

DATE               datetime64[ns]
TIME                       object
PICK UP ADDRESS            object
dtype: object

other_Firstclass_B01536.shape

(166769, 3)

other_Firstclass_B01536.isnull().sum()

DATE               0
TIME               0
PICK UP ADDRESS    0
dtype: int64

other_Highclass_B01717.head()

other_Highclass_B01717.head()

other_Highclass_B01717['DATE'] = pd.to_datetime(other_Highclass_B01717['DATE'])

other_Highclass_B01717.dtypes

DATE          datetime64[ns]
TIME                  object
PU_Address            object
dtype: object

other_Highclass_B01717.isnull().sum()

DATE          0
TIME          0
PU_Address    0
dtype: int64

other_Lyft_B02510.head()

other_Lyft_B02510['time_of_trip'] = pd.to_datetime(other_Lyft_B02510['time_of_trip'])

other_Lyft_B02510.dtypes

time_of_trip    datetime64[ns]
start_lat              float64
start_lng              float64
Unnamed: 3              object
dtype: object

other_Lyft_B02510.isnull().sum()

time_of_trip         0
start_lat            1
start_lng            1
Unnamed: 3      266503
dtype: int64

other_Lyft_B02510.dropna()

other_Lyft_B02510.shape

(267701, 4)

other_Prestige_B01338.head()

other_Prestige_B01338['DATE'] = pd.to_datetime(other_Prestige_B01338['DATE'])

other_Prestige_B01338.dtypes

DATE               datetime64[ns]
TIME                       object
PICK UP ADDRESS            object
dtype: object

other_Prestige_B01338.isnull().sum()

DATE               0
TIME               0
PICK UP ADDRESS    0
dtype: int64

other_Prestige_B01338.shape

(320641, 3)

other_Skyline_B00111.head()

other_Skyline_B00111['Date'] = pd.to_datetime(other_Skyline_B00111['Date'])

other_Skyline_B00111.dtypes

Date                   datetime64[ns]
Time                           object
    Street_Address             object
    City_State                 object
Unnamed: 4                    float64
Unnamed: 5                    float64
dtype: object

other_Skyline_B00111.isnull().sum()

Date                        0
Time                        0
    Street_Address          0
    City_State              0
Unnamed: 4             127696
Unnamed: 5             127696
dtype: int64

other_Skyline_B00111.dropna()

other_Skyline_B00111.shape

(127696, 6)

uber_raw_data_apr14.head()

uber_raw_data_apr14['Date/Time'] = pd.to_datetime(uber_raw_data_apr14['Date/Time'])

uber_raw_data_apr14['year'] = uber_raw_data_apr14['Date/Time'].dt.year
uber_raw_data_apr14['month'] = uber_raw_data_apr14['Date/Time'].dt.month_name()
uber_raw_data_apr14['day'] = uber_raw_data_apr14['Date/Time'].dt.day_name()
uber_raw_data_apr14['hours'] = uber_raw_data_apr14['Date/Time'].dt.hour

uber_raw_data_apr14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_apr14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_apr14.head()

uber_raw_data_apr14.shape

(564516, 8)

uber_raw_data_aug14.head()

uber_raw_data_aug14['Date/Time'] = pd.to_datetime(uber_raw_data_aug14['Date/Time'])

uber_raw_data_aug14['year'] = uber_raw_data_aug14['Date/Time'].dt.year
uber_raw_data_aug14['month'] = uber_raw_data_aug14['Date/Time'].dt.month_name()
uber_raw_data_aug14['day'] = uber_raw_data_aug14['Date/Time'].dt.day_name()
uber_raw_data_aug14['hours'] = uber_raw_data_aug14['Date/Time'].dt.hour

uber_raw_data_aug14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_aug14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_aug14.head()

uber_raw_data_aug14.shape

(829275, 8)

uber_raw_data_jul14.head()

uber_raw_data_jul14['Date/Time'] = pd.to_datetime(uber_raw_data_jul14['Date/Time'])

uber_raw_data_jul14['year'] = uber_raw_data_jul14['Date/Time'].dt.year
uber_raw_data_jul14['month'] = uber_raw_data_jul14['Date/Time'].dt.month_name()
uber_raw_data_jul14['day'] = uber_raw_data_jul14['Date/Time'].dt.day_name()
uber_raw_data_jul14['hours'] = uber_raw_data_jul14['Date/Time'].dt.hour

uber_raw_data_jul14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_jul14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_jul14.shape

(796121, 8)

uber_raw_data_jun14.head()

uber_raw_data_jun14['Date/Time'] = pd.to_datetime(uber_raw_data_jun14['Date/Time'])

uber_raw_data_jun14['year'] = uber_raw_data_jun14['Date/Time'].dt.year
uber_raw_data_jun14['month'] = uber_raw_data_jun14['Date/Time'].dt.month_name()
uber_raw_data_jun14['day'] = uber_raw_data_jun14['Date/Time'].dt.day_name()
uber_raw_data_jun14['hours'] = uber_raw_data_jun14['Date/Time'].dt.hour

uber_raw_data_jun14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_jun14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_jun14.head()

uber_raw_data_jun14.shape

(663844, 8)

uber_raw_data_may14.head()

uber_raw_data_may14['Date/Time'] = pd.to_datetime(uber_raw_data_may14['Date/Time'])

uber_raw_data_may14['year'] = uber_raw_data_may14['Date/Time'].dt.year
uber_raw_data_may14['month'] = uber_raw_data_may14['Date/Time'].dt.month_name()
uber_raw_data_may14['day'] = uber_raw_data_may14['Date/Time'].dt.day_name()
uber_raw_data_may14['hours'] = uber_raw_data_may14['Date/Time'].dt.hour

uber_raw_data_may14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_may14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_may14.head()

uber_raw_data_may14.shape

(652435, 8)

uber_raw_data_sep14.head()

uber_raw_data_sep14['Date/Time'] = pd.to_datetime(uber_raw_data_sep14['Date/Time'])

uber_raw_data_sep14['year'] = uber_raw_data_sep14['Date/Time'].dt.year
uber_raw_data_sep14['month'] = uber_raw_data_sep14['Date/Time'].dt.month_name()
uber_raw_data_sep14['day'] = uber_raw_data_sep14['Date/Time'].dt.day_name()
uber_raw_data_sep14['hours'] = uber_raw_data_sep14['Date/Time'].dt.hour

uber_raw_data_sep14.dtypes

Date/Time    datetime64[ns]
Lat                 float64
Lon                 float64
Base                 object
year                  int32
month                object
day                  object
hours                 int32
dtype: object

uber_raw_data_sep14.isnull().sum()

Date/Time    0
Lat          0
Lon          0
Base         0
year         0
month        0
day          0
hours        0
dtype: int64

uber_raw_data_sep14.head()

uber_raw_data_sep14.shape

(1028136, 8)

pd.read_csv('other-American_B01362.csv').to_csv('other-American_B01362.csv', index=False)

pd.read_csv('other-Carmel_B00256.csv',encoding = 'ISO-8859-1').to_csv('other-Carmel_B00256.csv',encoding ='ISO-8859-1',index=False)

pd.read_csv('other-Dial7_B00887.csv').to_csv('other-Dial7_B00887.csv', index=False)

pd.read_csv('other-Diplo_B01196.csv',encoding = 'ISO-8859-1').to_csv('other-Diplo_B01196.csv', encoding = 'ISO-8859-1',index=False)

pd.read_csv('other-Federal_02216.csv').to_csv('other-Federal_02216.csv', index=False)

pd.read_csv('other-Firstclass_B01536.csv', encoding = 'ISO-8859-1').to_csv('other-Firstclass_B01536.csv', encoding = 'ISO-8859-1', index=False)

pd.read_csv('other-Highclass_B01717.csv',encoding = 'ISO-8859-1').to_csv('other-Highclass_B01717.csv',encoding = 'ISO-8859-1', index=False)

pd.read_csv('other-Lyft_B02510.csv').to_csv('other-Lyft_B02510.csv', index=False)

pd.read_csv('other-Prestige_B01338.csv', encoding = 'ISO-8859-1').to_csv('other-Prestige_B01338.csv', encoding = 'ISO-8859-1', index=False)

pd.read_csv('other-Skyline_B00111.csv').to_csv('other-Skyline_B00111.csv', index=False)

pd.read_csv('uber-raw-data-apr14.csv').to_csv('uber-raw-data-apr14.csv', index=False)

pd.read_csv('uber-raw-data-aug14.csv').to_csv('uber-raw-data-aug14.csv', index=False)

pd.read_csv('uber-raw-data-jul14.csv').to_csv('uber-raw-data-jul14.csv', index=False)

pd.read_csv('uber-raw-data-jun14.csv').to_csv('uber-raw-data-jun14.csv', index=False)

pd.read_csv('uber-raw-data-may14.csv').to_csv('uber-raw-data-may14.csv', index=False)

pd.read_csv('uber-raw-data-sep14.csv').to_csv('uber-raw-data-sep14.csv', index=False)

pd.read_csv('uber-raw-data-janjune-15.csv').to_csv('uber-raw-data-janjune-15.csv', index=False)

pd.read_csv('other-FHV-services_jan-aug-2015.csv').to_csv('other-FHV-services_jan-aug-2015.csv', index=False)

pd.read_csv('Uber-Jan-Feb-FOIL.csv').to_csv('Uber-Jan-Feb-FOIL.csv', index=False)

# other_Skyline_B00111.columns = other_Skyline_B00111.columns.str.strip()  # this is for Remove leading/trailing spaces

# Uber Data Modeling - Fact and Dimension Tables

# Load data into DataFrames
other_American_B01362 = pd.read_csv('other-American_B01362.csv')
other_Carmel_B00256 = pd.read_csv('other-Carmel_B00256.csv',encoding = 'ISO-8859-1')
other_Dial7_B00887 = pd.read_csv('other-Dial7_B00887.csv')
other_Diplo_B01196 = pd.read_csv('other-Diplo_B01196.csv',encoding = 'ISO-8859-1')
other_Federal_02216 = pd.read_csv('other-Federal_02216.csv')
other_Firstclass_B01536 = pd.read_csv('other-Firstclass_B01536.csv',encoding = 'ISO-8859-1')
other_Highclass_B01717 = pd.read_csv('other-Highclass_B01717.csv',encoding = 'ISO-8859-1')
other_Lyft_B02510 = pd.read_csv('other-Lyft_B02510.csv')
other_Prestige_B01338 = pd.read_csv('other-Prestige_B01338.csv',encoding = 'ISO-8859-1')
other_Skyline_B00111 = pd.read_csv('other-Skyline_B00111.csv')
uber_raw_data_apr14 = pd.read_csv('uber-raw-data-apr14.csv')
uber_raw_data_aug14 = pd.read_csv('uber-raw-data-aug14.csv')
uber_raw_data_jul14 = pd.read_csv('uber-raw-data-jul14.csv')
uber_raw_data_jun14 = pd.read_csv('uber-raw-data-jun14.csv')
uber_raw_data_may14 = pd.read_csv('uber-raw-data-may14.csv')
uber_raw_data_sep14 = pd.read_csv('uber-raw-data-sep14.csv')
uber_raw_data_janjune_15 = pd.read_csv('uber-raw-data-janjune-15.csv')
other_FHV_services_jan_aug_2015 = pd.read_csv('other-FHV-services_jan-aug-2015.csv')
Uber_Jan_Feb_FOIL = pd.read_csv('Uber-Jan-Feb-FOIL.csv')



# Fact Table: Trips and Vehicles
fact_trips_vehicles = pd.concat([
    other_FHV_services_jan_aug_2015[['Base Number', 'Pick Up Date', 'Number of Trips', 'Number of Vehicles']],
    Uber_Jan_Feb_FOIL[['dispatching_base_number', 'date', 'trips']].rename(columns={
        'dispatching_base_number': 'Base Number',
        'date': 'Pick Up Date',
        'trips': 'Number of Trips'
    })
], ignore_index=True)

# Dimension Table: Date
dimension_date = pd.DataFrame({
    'Date': pd.concat([
        pd.to_datetime(other_American_B01362['DATE'], errors='coerce'),
        pd.to_datetime(other_Carmel_B00256['Date'], errors='coerce'),
        pd.to_datetime(other_Dial7_B00887['Date'], errors='coerce'),
        pd.to_datetime(other_Diplo_B01196['Date'], errors='coerce'),
        pd.to_datetime(other_Federal_02216['Date'], errors='coerce'),
        pd.to_datetime(other_Firstclass_B01536['DATE'], errors='coerce'),
        pd.to_datetime(other_Highclass_B01717['DATE'], errors='coerce'),
        pd.to_datetime(uber_raw_data_janjune_15['Pickup_date'], errors='coerce')
    ]).dropna().drop_duplicates().reset_index(drop=True)
})



# Dimension Table: Location
location_columns = ['PICK UP ADDRESS', 'PU_Adress', 'PU_Address', 'PU_Address.1', 'Street_Address']
location_data = pd.concat([
    other_American_B01362['PICK UP ADDRESS'],
    other_Carmel_B00256['PU_Adress'],
    other_Dial7_B00887['Address'],
    other_Diplo_B01196['PU_Address'],
    other_Federal_02216['PU_Address'],
    other_Firstclass_B01536['PICK UP ADDRESS'],
    other_Highclass_B01717['PU_Address'],
    other_Skyline_B00111['    Street_Address ']
], ignore_index=True).dropna().drop_duplicates()
dimension_location = pd.DataFrame({'Location': location_data.reset_index(drop=True)})



# Dimension Table: Base
base_columns = ['Base_No', 'Base Number', 'dispatching_base_number', 'Affiliated_base_num', 'Base']
base_data = pd.concat([
    other_Carmel_B00256['Base_No'],
    other_FHV_services_jan_aug_2015['Base Number'],
    Uber_Jan_Feb_FOIL['dispatching_base_number'],
    uber_raw_data_janjune_15['Affiliated_base_num'],
    uber_raw_data_apr14['Base'],
    uber_raw_data_aug14['Base'],
    uber_raw_data_jul14['Base'],
    uber_raw_data_jun14['Base'],
    uber_raw_data_may14['Base'],
    uber_raw_data_sep14['Base']
], ignore_index=True).dropna().drop_duplicates()
dimension_base = pd.DataFrame({'Base': base_data.reset_index(drop=True)})



# Save the tables for future use
fact_trips_vehicles.to_csv('fact_trips_vehicles.csv', index=False)
dimension_date.to_csv('dimension_date.csv', index=False)
dimension_location.to_csv('dimension_location.csv', index=False)
dimension_base.to_csv('dimension_base.csv', index=False)

fact_trips_vehicles = pd.read_csv("fact_trips_vehicles.csv")
fact_trips_vehicles

dimension_date = pd.read_csv("dimension_date.csv")
dimension_date

dimension_location = pd.read_csv("dimension_location.csv")
dimension_location

dimension_base = pd.read_csv("dimension_base.csv")
dimension_base

files = []

for dirname, _, filenames in os.walk(r'C:\Users\Prabhushankar\OneDrive\Desktop\Uber Trip Analysis Machine Learning'):
    for filename in filenames:
        files.append(os.path.join(dirname, filename)) if "raw" in filename else None

files = files[:-1]

dataframes = [pd.read_csv(file) for file in files]

uber2014 = pd.concat(dataframes, ignore_index=True)

if 'Date/Time' in uber2014.columns:
    uber2014['Date/Time'] = pd.to_datetime(uber2014['Date/Time'], format='%m/%d/%Y %H:%M:%S')

if 'Date/Time' in uber2014.columns:
    uber2014 = uber2014.sort_values(by='Date/Time')

if 'Date/Time' in uber2014.columns:
    uber2014.set_index('Date/Time', inplace=True)

uber2014.columns

Index(['Lat', 'Lon', 'Base', 'Dispatching_base_num', 'Pickup_date',
       'Affiliated_base_num', 'locationID'],
      dtype='object')

hourly_counts = uber2014['Base'].resample('h').count()

uber2014 = hourly_counts.reset_index()
# Rename columns for clarity
uber2014.columns = ['Date', 'Count']
uber2014.set_index('Date',inplace=True)

uber2014.columns

Index(['Count'], dtype='object')

uber2014.head()

dimension_date['Date'] = pd.to_datetime(dimension_date['Date'])
fact_trips_vehicles['Pick Up Date'] = pd.to_datetime(fact_trips_vehicles['Pick Up Date'])

merged_data = pd.merge(dimension_date, fact_trips_vehicles, left_on='Date', right_on='Pick Up Date', how='inner')

# for Jan 2015
merged_data['Year_Month'] = merged_data['Date'].dt.to_period('M') 
january_data = merged_data[merged_data['Year_Month'] == '2015-01']

data = january_data[['Date', 'Number of Trips']].dropna()

data['Number of Trips'] = data['Number of Trips'].replace({',': ''}, regex=True).astype(float)


data_sorted = data.sort_values(by='Date')

total_trips = data_sorted['Number of Trips'].sum()

# Create Line Chart for Number of Trips with Seaborn styling
plt.figure(figsize=(12, 6))
plt.plot(data_sorted['Date'], data_sorted['Number of Trips'], marker='x',markerfacecolor='red', color='Teal', label='Number of Trips', linestyle='-', linewidth=2)
plt.title(f'Trips in January 2015 (Total: {total_trips})', fontsize=16, weight='bold')
plt.xlabel('Date', fontsize=12, weight='bold')
plt.ylabel('Number of Trips', fontsize=12, weight='bold')
plt.xticks(rotation=45)
plt.legend(loc='upper left', fontsize=12)
plt.grid(True, alpha=0.5)
plt.tight_layout()
mplcursors.cursor(hover=True)

plt.show()

uber_raw_data_janjune_15['day'].value_counts()

day
Saturday     2227660
Friday       2111389
Thursday     1985749
Sunday       1816566
Wednesday    1751009
Tuesday      1732528
Monday       1586651
Name: count, dtype: int64

uber_raw_data_janjune_15['day'].value_counts().plot(kind ="bar")

<Axes: xlabel='day'>

uber_raw_data_janjune_15['month'].value_counts()

month
June        2571771
May         2483980
February    2141306
April       2112705
March       2062639
January     1839151
Name: count, dtype: int64

uber_raw_data_janjune_15['month'].value_counts().plot(kind = 'bar')

<Axes: xlabel='month'>

demand_days_for_Uber = pd.crosstab(index = uber_raw_data_janjune_15['month'], columns=uber_raw_data_janjune_15['day']) #grouped bar plot using crosstab
demand_days_for_Uber

demand_days_for_Uber.plot(kind = 'line', figsize = (12,8))

<Axes: xlabel='month'>

demand_days_for_Uber.plot(kind = 'bar', figsize = (12,8))

<Axes: xlabel='month'>

day_hours_summary = uber_raw_data_janjune_15.groupby(by = ['day','hours'], as_index= False).size()
day_hours_summary

plt.figure(figsize=(10,8))
sns.pointplot(data = day_hours_summary, x = 'hours', y = 'size',hue='day')

<Axes: xlabel='hours', ylabel='size'>

table_name = "fact_trips_vehicles"
x_column = "Pick Up Date"
y_column = "Number of Trips"

fact_trips_vehicles[y_column] = fact_trips_vehicles[y_column].astype(str).str.replace(",", "").str.strip().astype(int)

# Filtering the data for getting March 2015
filtered_data = fact_trips_vehicles[
    (fact_trips_vehicles[x_column] >= "2015-03-01") & 
    (fact_trips_vehicles[x_column] <= "2015-03-31")
]

aggregated_data = filtered_data.groupby(x_column)[y_column].sum().reset_index()

# Scatter plot
plt.figure(figsize=(12, 6))
plt.scatter(
    aggregated_data[x_column], 
    aggregated_data[y_column], 
    alpha=0.8,  
    edgecolors='black'
)

plt.title(f"Scatter Chart: {table_name} - March 2015", fontsize=14)
plt.xlabel("Pick Up Date", fontsize=12)
plt.ylabel("Trips", fontsize=12)

# Remove tick labels (values) from y-axis
    # plt.gca().set_yticklabels([])
    # plt.gca().set_xticklabels([])

for i, row in aggregated_data.iterrows():
    plt.text(
        row[x_column], 
        row[y_column], 
        f"{row[y_column]:,}",  # Format as an integer with thousands separator
        fontsize=9, 
        ha='center', 
        va='bottom', 
        color="black"
    )

plt.grid(True, color="red", linestyle="--", linewidth=0.7)

# Set face and edge colors
plt.gca().set_facecolor("#ffffff")  # Light cement-like background
plt.gca().spines['bottom'].set_color('red')
plt.gca().spines['left'].set_color('coral')

plt.tight_layout()  # Prevent label cut-offs
plt.show()

Uber_Jan_Feb_FOIL.head()

Uber_Jan_Feb_FOIL.columns

Index(['dispatching_base_number', 'date', 'active_vehicles', 'trips'], dtype='object')

plt.figure(figsize= (10,8))
sns.boxplot(data = Uber_Jan_Feb_FOIL, x = 'dispatching_base_number', y = 'active_vehicles')
plt.show()

filenames = ["uber-raw-data-apr14.csv","uber-raw-data-aug14.csv","uber-raw-data-jul14.csv","uber-raw-data-jun14.csv","uber-raw-data-may14.csv","uber-raw-data-sep14.csv"]

raw_data = []

for fname in filenames:
    df = pd.read_csv(fname)
    raw_data.append(df)

final_raw_data = pd.concat(raw_data,ignore_index = True)
final_raw_data

final_raw_data.shape

(4534327, 4)

final_raw_data.columns

Index(['Date/Time', 'Lat', 'Lon', 'Base'], dtype='object')

final_raw_data.head()

final_raw_data.tail()

final_raw_data.dtypes

Date/Time     object
Lat          float64
Lon          float64
Base          object
dtype: object

final_raw_data.duplicated().sum()

np.int64(82581)

final_raw_data.drop_duplicates(inplace = True)

final_raw_data.duplicated().sum()

np.int64(0)

lat_lan_group = final_raw_data.groupby(['Lat','Lon'],as_index = False).size()   #as_index = False makes the attribute size() as its index name
lat_lan_group

basemap = folium.Map()
basemap

from folium.plugins import HeatMap

HeatMap(lat_lan_group).add_to(basemap)

<folium.plugins.heat_map.HeatMap at 0x2c53df597f0>

basemap

final_raw_data.head()

final_raw_data['Date/Time'] = pd.to_datetime(final_raw_data['Date/Time'], format= "%m/%d/%Y %H:%M:%S")

final_raw_data['Day'] = final_raw_data['Date/Time'].dt.day
final_raw_data['Month'] = final_raw_data['Date/Time'].dt.month
final_raw_data['Hour'] = final_raw_data['Date/Time'].dt.hour

final_raw_data.head()

pivot_table = final_raw_data.groupby(['Day','Hour']).size().unstack()
pivot_table

pivot_table.style.background_gradient()

pivot_table.style.background_gradient(cmap='coolwarm')

uber2014.head()

plt.figure(figsize=(20, 8))
plt.plot(uber2014['Count'],linewidth = 1, color='darkslateblue')
plt.xticks(rotation=30,ha='right')
plt.show()

result = seasonal_decompose(uber2014['Count'], model='add', period=24)

plt.figure(figsize=(20, 16))  

# Observed
plt.subplot(4, 1, 1)
plt.plot(result.observed, label='Observed', color='blue')
plt.title('Observed', fontsize=16, weight='bold')
plt.grid(True)
plt.legend()

# Trend
plt.subplot(4, 1, 2)
plt.plot(result.trend, label='Trend', color='green')
plt.title('Trend', fontsize=16, weight='bold')
plt.grid(True)
plt.legend()

# Seasonal
plt.subplot(4, 1, 3)
plt.plot(result.seasonal, label='Seasonal', color='Gold')
plt.title('Seasonal', fontsize=16, weight='bold')
plt.grid(True)
plt.legend()

# Residual
plt.subplot(4, 1, 4)
plt.plot(result.resid, label='Residual', color='red')
plt.title('Residual', fontsize=16, weight='bold')
plt.grid(True)
plt.legend()

plt.tight_layout()
plt.show()

cutoff_date = '2014-08-15 00:00:00' #This is defining a specific cutoff date, you can change this
plt.figure(figsize=(18, 8))
plt.plot(result.trend,linewidth = 1, color='black')
plt.axvline(x=pd.Timestamp(cutoff_date), color='red', linestyle='--', linewidth=2)
plt.xticks(rotation=30,ha='right')
plt.show()

uber2014_train = uber2014.loc[:cutoff_date]
uber2014_test = uber2014.loc[cutoff_date:]

uber2014_test.rename(columns={'Count':'TEST SET'}).join(uber2014_train.rename(columns={'Count':'TRAINING SET'}), how='outer').plot(figsize=(15,5), title='Train / Test Sets', style='--', lw=1)

<Axes: title={'center': 'Train / Test Sets'}, xlabel='Date'>

window_size = 24

def create_lagged_features(data, window_size):

    X, y = [], []
    for i in range(window_size, len(data)):
        X.append(data[i-window_size:i])  # Lagged values
        y.append(data[i])  # Current value
    return np.array(X), np.array(y)

X_train, y_train = create_lagged_features(uber2014_train['Count'].values, window_size)

X_train, y_train = create_lagged_features(uber2014_train['Count'].values, window_size)

test_data = np.concatenate([uber2014_train['Count'].values[-window_size:], uber2014_test['Count'].values])
X_test, y_test = create_lagged_features(test_data, window_size)

seed = 12345 # sets a random seed for generating random numbers

uber2014.head()

uber2014.dtypes

Count    int64
dtype: object

print(uber2014.index.min())  # Earliest date in the dataset
print(uber2014.index.max())  # Latest date in the dataset

2014-04-01 00:00:00
2014-08-31 23:00:00

split_ratio = 0.9  
split_point = int(len(uber2014) * split_ratio)
train = uber2014.iloc[:split_point]
test = uber2014.iloc[split_point:]

print("Length of train:", len(train))
print("Length of test:", len(test))

def create_lagged_features(series, window_size):
    X, y = [], []
    if len(series) > window_size:
        for i in range(len(series) - window_size):
            X.append(series[i:i+window_size])
            y.append(series[i+window_size])
    return np.array(X), np.array(y)

window_size = 24

X_train, y_train = create_lagged_features(train['Count'].values, window_size)

test_series = np.concatenate([train['Count'].values[-window_size:], test['Count'].values])
X_test, y_test = create_lagged_features(test_series, window_size)

if X_train.ndim == 1:
    X_train = X_train.reshape(-1, 1)
if X_test.ndim == 1:
    X_test = X_test.reshape(-1, 1)

rf_model = RandomForestRegressor(
    n_estimators=200,
    max_depth=20,
    min_samples_split=5,
    random_state=42
)
rf_model.fit(X_train, y_train)
rf_predictions = rf_model.predict(X_test)

print(f"Random Forest MAPE: {mean_absolute_percentage_error(y_test, rf_predictions):.2%}")

Length of train: 3304
Length of test: 368
Random Forest MAPE: 6.92%

def plot_predictions(y_test, predictions, title):
    plt.figure(figsize=(12, 6))
    plt.plot(range(len(y_test)), y_test, label='Actual', color='blue', alpha=0.7)
    plt.plot(range(len(predictions)), predictions, label='Predicted', color='orange', linestyle='--')
    plt.title(title)
    plt.xlabel('Time Steps')
    plt.ylabel('Trips')
    plt.legend()
    plt.show()

plot_predictions(y_test, rf_predictions, "Random Forest: Actual vs Predicted Trips")

print(f"Random Forest MAPE: {mean_absolute_percentage_error(y_test, rf_predictions):.2%}")

Random Forest MAPE: 6.92%

# Gradient Boosted Regressor Model
gbr_model = GradientBoostingRegressor(
    n_estimators=200,
    learning_rate=0.1,
    max_depth=5,
    random_state=42
)
gbr_model.fit(X_train, y_train)
gbr_predictions = gbr_model.predict(X_test)

# Plot Actual vs Predicted Values for GBR
def plot_gbr_predictions(y_test, gbr_predictions):
    plt.figure(figsize=(12, 6))
    plt.plot(range(len(y_test)), y_test, label='Actual', color='blue', alpha=0.7)
    plt.plot(range(len(gbr_predictions)), gbr_predictions, label='GBR Predicted', color='gold', linestyle='--')
    plt.title("Gradient Boosted Regressor: Actual vs Predicted Trips")
    plt.xlabel('Time Steps')
    plt.ylabel('Trips')
    plt.legend()
    plt.show()

plot_gbr_predictions(y_test, gbr_predictions)

print(f"Gradient Boosted Regressor MAPE: {mean_absolute_percentage_error(y_test, gbr_predictions):.2%}")

Gradient Boosted Regressor MAPE: 7.26%

weights = np.array([1 / mean_absolute_percentage_error(y_test, rf_predictions),
                    1 / mean_absolute_percentage_error(y_test, gbr_predictions)])
weights /= weights.sum()  # Normalize weights
ensemble_predictions = (weights[0] * rf_predictions +
                        weights[1] * gbr_predictions)

print(f"Ensemble MAPE: {mean_absolute_percentage_error(y_test, ensemble_predictions):.2%}")

Ensemble MAPE: 6.90%

# Visualization for the Ensemble Model
def plot_ensemble_predictions(y_test, ensemble_predictions):
    plt.figure(figsize=(12, 6))
    plt.plot(range(len(y_test)), y_test, label='Actual', color='blue', alpha=0.7)
    plt.plot(range(len(ensemble_predictions)), ensemble_predictions, label='Ensemble Predicted', color='yellow', linestyle='--')
    plt.title("Ensemble Model (RF + GBR): Actual vs Predicted Trips")
    plt.xlabel('Time Steps')
    plt.ylabel('Trips')
    plt.legend()
    plt.show()

plot_ensemble_predictions(y_test, ensemble_predictions)
print(f"Ensemble MAPE: {mean_absolute_percentage_error(y_test, ensemble_predictions):.2%}")

Ensemble MAPE: 6.90%

print(f"Random Forest MAPE: {mean_absolute_percentage_error(y_test, rf_predictions):.2%}")
print(f"Gradient Boosted Regressor MAPE: {mean_absolute_percentage_error(y_test, gbr_predictions):.2%}")
print(f"Ensemble MAPE: {mean_absolute_percentage_error(y_test, ensemble_predictions):.2%}")

Random Forest MAPE: 6.92%
Gradient Boosted Regressor MAPE: 7.26%
Ensemble MAPE: 6.90%

	dispatching_base_number	date	active_vehicles	trips
0	B02512	1/1/2015	190	1132
1	B02765	1/1/2015	225	1765
2	B02764	1/1/2015	3427	29421
3	B02682	1/1/2015	945	7679
4	B02617	1/1/2015	1228	9537
...	...	...	...	...
349	B02764	2/28/2015	3952	39812
350	B02617	2/28/2015	1372	14022
351	B02682	2/28/2015	1386	14472
352	B02512	2/28/2015	230	1803
353	B02765	2/28/2015	747	7753

	DATE	TIME	PICK UP ADDRESS	Unnamed: 3	Unnamed: 4	Unnamed: 5
0	7/1/2014	12:00:00 AM	874 E 139th St Mott Haven, BX	NaN	NaN	NaN
1	7/1/2014	12:01:00 AM	628 E 141st St Mott Haven, BX	NaN	NaN	NaN
2	7/1/2014	12:01:00 AM	601 E 156th St South Bronx, BX	NaN	NaN	NaN
3	7/1/2014	12:01:00 AM	708 E 138th St Mott Haven, BX	NaN	NaN	NaN
4	7/1/2014	12:02:00 AM	700 E 140th St Mott Haven, BX	NaN	NaN	NaN
...	...	...	...	...	...	...
91707	9/30/2014	11:47:00 PM	593 Oak Ter Mott Haven, BX	NaN	NaN	NaN
91708	9/30/2014	11:48:00 PM	645 Westchester Ave South Bronx, BX	NaN	NaN	NaN
91709	9/30/2014	11:51:00 PM	625 Tinton Ave South Bronx, BX	NaN	NaN	NaN
91710	9/30/2014	11:54:00 PM	350 Saint Anns Ave Mott Haven, BX	NaN	NaN	NaN
91711	9/30/2014	11:58:00 PM	846 Tinton Ave Morrisania, BX	NaN	NaN	NaN

	Date	Time	State	PuFrom	Address	Street
0	2014.07.06	14:30	NY ...	MANHATTAN	50	MURRAY ST
1	2014.07.04	7:15	NY ...	MANHATTAN	143	AVENUE B
2	2014.07.05	5:45	NY ...	MANHATTAN	125	CHRISTOPHER ST
3	2014.07.06	4:30	NY ...	MANHATTAN	217	E 7 ST
4	2014.07.05	7:45	NY ...	MANHATTAN	521	W 26 ST
...	...	...	...	...	...	...
194987	2014.09.28	21:50	JFK DELTA TERM 4 P/U AREA C ...	Jfk
194988	2014.09.28	7:00	NY ...	MANHATTAN	112	W 78 ST
194989	2014.09.28	15:30	NY ...	MANHATTAN	601	W 57 ST
194990	2014.09.28	19:55	JFK DELTA TERM 2 CURBSIDE ...	NaN
194991	2014.09.08	9:45	NY ...	MANHATTAN	415	W 24 ST

	Date	Time	PU_Address
0	7/1/2014	12:00:00 AM	2396 Valentine Ave Fordham, BX
1	7/1/2014	12:01:00 AM	1859 Walton Ave Morris Heights, BX
2	7/1/2014	12:02:00 AM	2431 Jerome Ave Jerome, BX
3	7/1/2014	12:05:00 AM	3044 Valentine Ave Fordham, BX
4	7/1/2014	12:05:00 AM	1085 Nelson Ave High Bridge, BX
...	...	...	...
98545	9/30/2014	11:54:00 PM	2847 Webb Ave Jerome, BX
98546	9/30/2014	11:56:00 PM	-BASE- 170 W Fordham Rd Jerome, BX
98547	9/30/2014	11:57:00 PM	-BASE- 170 W Fordham Rd Jerome, BX
98548	9/30/2014	11:57:00 PM	-BASE- 170 W Fordham Rd Jerome, BX
98549	9/30/2014	11:58:00 PM	-BASE- 170 W Fordham Rd Jerome, BX

	Base Number	Base Name	Pick Up Date	Number of Trips	Number of Vehicles
0	B00013	LOVE CORP CAR INC	01/01/2015	26	17
1	B00014	NY ONE CORP CAR INC	01/01/2015	45	24
2	B00029	COMMUNITY CAR SVC CORP	01/01/2015	731	36
3	B00053	CHARGE AND RIDE INC	01/01/2015	10	9
4	B00095	LIBERTY CAR SERVICE INC.	01/01/2015	814	62
...	...	...	...	...	...
26176	B02666	D.L.C. LIMOUSINE SERVICE, INC.	08/30/2015	4	3
26177	B01313	JOY CAR SERVICES INC.	08/31/2015	14	14
26178	B01509	BRUNWISH ENTERPRISES,LTD.	08/31/2015	736	63
26179	B01614	R TRANSPORT INC	08/31/2015	45	8
26180	B02666	D.L.C. LIMOUSINE SERVICE, INC.	08/31/2015	6	6

UBER TRIP ANALYSIS¶

IMPORTING necessary Python Libraries and Files¶

DATA CLEANING¶

ON uber_raw_data_jul14 file¶

Saving all files using #to_csv¶

MODELING the Data¶

DATA VISUALIZATION¶

Machine Learning Models¶

Mean Absolute Percentage Error(MAPE) of all Models¶

Insights and Conclusions from Training and Evaluation¶¶

	Date	Time	PU_Adress	Base_No
0	7/1/2014	0:00	260 W 44 St NYC	B00256
1	7/1/2014	0:00	125 W 29 St Nyc	B00256
2	7/1/2014	0:00	141 W 28 St Nyc	B00256
3	7/1/2014	0:01	EWR	B00256
4	7/1/2014	0:07	JFK	B00256
...	...	...	...	...
256514	9/30/2014	23:53	JFK	B00256
256515	9/30/2014	23:55	5 Allen St Nyc	B00256
256516	9/30/2014	23:56	JFK	B00256
256517	9/30/2014	23:58	EWR	B00256
256518	9/30/2014	23:59	JFK	B00256

	Date	Time	PU_Address	DO_Address	Routing Details	PU_Address.1	Status
0	07/01/2014	07:15 AM	Brooklyn Museum, 200 Eastern Pkwy., BK NY;	1 Brookdale Plaza, BK NY;	PU: Brooklyn Museum, 200 Eastern Pkwy., BK NY;...	Brooklyn Museum, 200 Eastern Pkwy., BK NY; DO:...	Cancelled
1	07/01/2014	07:30 AM	33 Robert Dr., Short Hills NJ;	John F Kennedy International Airport, vitona A...	PU: 33 Robert Dr., Short Hills NJ; DO: John F ...	33 Robert Dr., Short Hills NJ; DO: John F Kenn...	Arrived
2	07/01/2014	08:00 AM	60 Glenmore Ave., BK NY;	2171 Nostrand Ave., BK NY;	PU: 60 Glenmore Ave., BK NY; DO: 2171 Nostrand...	60 Glenmore Ave., BK NY; DO: 2171 Nostrand Ave...	Assigned
3	07/01/2014	09:00 AM	128 East 31 St., BK NY;	369 93rd St., BK NY;	PU: 128 East 31 St., BK NY; DO: 369 93rd St., ...	128 East 31 St., BK NY; DO: 369 93rd St., BK NY;	Assigned
4	07/01/2014	09:30 AM	139-39 35 Ave., Flushing NY;	La Guardia Airport;	PU: 139-39 35 Ave., Flushing NY; DO: La Guardi...	139-39 35 Ave., Flushing NY; DO: La Guardia Ai...	Assigned
...	...	...	...	...	...	...	...
271	09/25/2014	12:00 PM	907 59 St., BK NY; ST: W66 St./Broadway, NY NY;	John F Kennedy International Airport;	PU: 907 59 St., BK NY; ST: W66 St./Broadway, N...	907 59 St., BK NY; ST: W66 St./Broadway, NY NY...	Arrived
272	09/26/2014	04:00 PM	20 West St., NY NY;	La Guardia Airport;	PU: 20 West St., NY NY; DO: La Guardia Airport;	20 West St., NY NY; DO: La Guardia Airport;	Assigned
273	09/27/2014	05:00 AM	474 Sackett Street, BK NY;	John F Kennedy International Airport, Virgin A...	PU: 474 Sackett Street, BK NY; DO: John F Kenn...	474 Sackett Street, BK NY; DO: John F Kennedy ...	Arrived
274	09/27/2014	08:00 AM	474 Sackett Street, BK NY;	Newark Liberty International Airport, Virgin A...	PU: 474 Sackett Street, BK NY; DO: Newark Libe...	474 Sackett Street, BK NY; DO: Newark Liberty ...	Arrived
275	09/28/2014	09:00 PM	152 7 Ave., BK NY;	Grand Central Station ;	PU: 152 7 Ave., BK NY; DO: Grand Central Stati...	152 7 Ave., BK NY; DO: Grand Central Station ;	Assigned

	DATE	TIME	PICK UP ADDRESS
0	7/1/2014	12:02:00 AM	5360 Broadway Kingsbridge, BX
1	7/1/2014	12:02:00 AM	546 Isham St NYC
2	7/1/2014	12:03:00 AM	234 Bradhurst Ave NYC
3	7/1/2014	12:07:00 AM	99 Marble Hill Ave Bronx
4	7/1/2014	12:08:00 AM	120 Vermilyea Ave NYC
...	...	...	...
166764	9/30/2014	11:52:00 PM	LINCOLN HOSPITAL MAIN 234 E 149th St Cortlandt...
166765	9/30/2014	11:53:00 PM	la bodega 2051 8 Ave NYC
166766	9/30/2014	11:56:00 PM	hess gas station nw 9 Ave NYC
166767	9/30/2014	11:58:00 PM	stop & shop supermarket 5716 Broadway Bronx
166768	9/30/2014	11:59:00 PM	579 Fort Washington Ave NYC

	DATE	TIME	PU_Address
0	7/1/2014	12:00:00 AM	2976 Marion Ave Fordham, BX
1	7/1/2014	12:01:00 AM	780 Grand Concourse Cortlandt, BX
2	7/1/2014	12:01:00 AM	105 Elliot Pl High Bridge, BX
3	7/1/2014	12:03:00 AM	2825 Webb Ave Bronx
4	7/1/2014	12:04:00 AM	1878 Cedar Ave Morris Heights, BX
...	...	...	...
151920	9/30/2014	11:54:00 PM	2247 Grand Ave Morris Heights, BX
151921	9/30/2014	11:55:00 PM	320 Morris Ave Bronx
151922	9/30/2014	11:55:00 PM	3105 Decatur Ave Williamsbridge, BX
151923	9/30/2014	11:58:00 PM	2125 Cruger Ave Parkchester, BX
151924	9/30/2014	11:58:00 PM	2401 Cruger Ave Williamsbridge, BX

	time_of_trip	start_lat	start_lng	Unnamed: 3
0	9/4/2014 9:51	40.64705	-73.77988	NaN
1	8/27/2014 21:13	40.74916	-73.98373	NaN
2	9/4/2014 14:16	40.64065	-73.97594	NaN
3	9/4/2014 16:08	40.75002	-73.99514	NaN
4	8/28/2014 2:41	40.76715	-73.98636	NaN
...	...	...	...	...
267696	9/7/2014 3:19	40.71931	-73.99138	NaN
267697	9/7/2014 2:59	40.73868	-73.98862	NaN
267698	9/7/2014 1:55	40.71554	-73.94877	NaN
267699	9/5/2014 23:26	40.74667	-73.97445	NaN
267700	9/6/2014 22:19	40.74393	-73.78011	NaN

	DATE	TIME	PICK UP ADDRESS
0	7/1/2014	12:00:00 AM	2557 Marion Ave Fordham, BX
1	7/1/2014	12:00:00 AM	45 E Mosholu Pkwy N Williamsbridge, BX
2	7/1/2014	12:00:00 AM	458 E 143rd St Mott Haven, BX
3	7/1/2014	12:02:00 AM	MINIVAN SUV 2557 Marion Ave Fordham, BX
4	7/1/2014	12:02:00 AM	1117 Westchester Ave Foxhurst, BX
...	...	...	...
320636	9/30/2014	11:59:00 PM	2420 Williamsbridge Rd Baychester, BX
320637	9/30/2014	11:59:00 PM	2260 Washington Avenue, Bronx, NY, United 226...
320638	9/30/2014	11:59:00 PM	1009 Sackett Ave Parkchester, BX
320639	9/30/2014	11:59:00 PM	3163 Villa Ave Jerome, BX
320640	9/30/2014	11:59:00 PM	1457 Pelham Pkwy N, Bronx, NY 10469 1457 Pelha...

	Date	Time	Street_Address	City_State	Unnamed: 4	Unnamed: 5
0	7/1/2014	20:27	622 THIRD AV	M	NaN	NaN
1	7/1/2014	21:04	E 77TH ST	M	NaN	NaN
2	7/1/2014	22:20	67 WEST PALISADES BLVD	PALISADES PARK NJ	NaN	NaN
3	7/1/2014	12:28	130 MIDDLE NECK RD	SANDS POINT LI	NaN	NaN
4	7/1/2014	16:45	36 E 31ST ST	M	NaN	NaN
...	...	...	...	...	...	...
127691	9/29/2014	13:15	370 7th AVE	M	NaN	NaN
127692	9/29/2014	15:40	1 STATE ST PZ	M	NaN	NaN
127693	9/29/2014	20:07	730 5TH AVE	M	NaN	NaN
127694	9/29/2014	22:48	30 ROCKEFELLER PZ	M	NaN	NaN
127695	9/29/2014	16:00	904 LEXINGTON AV	M	NaN	NaN

	Date/Time	Lat	Lon	Base
0	4/1/2014 0:11:00	40.7690	-73.9549	B02512
1	4/1/2014 0:17:00	40.7267	-74.0345	B02512
2	4/1/2014 0:21:00	40.7316	-73.9873	B02512
3	4/1/2014 0:28:00	40.7588	-73.9776	B02512
4	4/1/2014 0:33:00	40.7594	-73.9722	B02512
...	...	...	...	...
564511	4/30/2014 23:22:00	40.7640	-73.9744	B02764
564512	4/30/2014 23:26:00	40.7629	-73.9672	B02764
564513	4/30/2014 23:31:00	40.7443	-73.9889	B02764
564514	4/30/2014 23:32:00	40.6756	-73.9405	B02764
564515	4/30/2014 23:48:00	40.6880	-73.9608	B02764

	Date/Time	Lat	Lon	Base
0	8/1/2014 0:03:00	40.7366	-73.9906	B02512
1	8/1/2014 0:09:00	40.7260	-73.9918	B02512
2	8/1/2014 0:12:00	40.7209	-74.0507	B02512
3	8/1/2014 0:12:00	40.7387	-73.9856	B02512
4	8/1/2014 0:12:00	40.7323	-74.0077	B02512
...	...	...	...	...
829270	8/31/2014 23:55:00	40.7552	-73.9753	B02764
829271	8/31/2014 23:55:00	40.7552	-73.9753	B02764
829272	8/31/2014 23:55:00	40.7617	-73.9788	B02764
829273	8/31/2014 23:59:00	40.7395	-73.9889	B02764
829274	8/31/2014 23:59:00	40.7270	-73.9802	B02764

	Dispatching_base_num	Pickup_date	Affiliated_base_num	locationID
0	B02617	2015-05-17 09:47:00	B02617	141
1	B02617	2015-05-17 09:47:00	B02617	65
2	B02617	2015-05-17 09:47:00	B02617	100
3	B02617	2015-05-17 09:47:00	B02774	80
4	B02617	2015-05-17 09:47:00	B02617	90
...	...	...	...	...
14270474	B02765	2015-05-08 15:43:00	B02765	186
14270475	B02765	2015-05-08 15:43:00	B02765	263
14270476	B02765	2015-05-08 15:43:00	B02765	90
14270477	B02765	2015-05-08 15:44:00	B01899	45
14270478	B02765	2015-05-08 15:44:00	B02682	144

	Date/Time	Lat	Lon	Base
0	7/1/2014 0:03:00	40.7586	-73.9706	B02512
1	7/1/2014 0:05:00	40.7605	-73.9994	B02512
2	7/1/2014 0:06:00	40.7320	-73.9999	B02512
3	7/1/2014 0:09:00	40.7635	-73.9793	B02512
4	7/1/2014 0:20:00	40.7204	-74.0047	B02512
...	...	...	...	...
796116	7/31/2014 23:22:00	40.7285	-73.9846	B02764
796117	7/31/2014 23:23:00	40.7615	-73.9868	B02764
796118	7/31/2014 23:29:00	40.6770	-73.9515	B02764
796119	7/31/2014 23:30:00	40.7225	-74.0038	B02764
796120	7/31/2014 23:58:00	40.7199	-73.9884	B02764

	Date/Time	Lat	Lon	Base
0	6/1/2014 0:00:00	40.7293	-73.9920	B02512
1	6/1/2014 0:01:00	40.7131	-74.0097	B02512
2	6/1/2014 0:04:00	40.3461	-74.6610	B02512
3	6/1/2014 0:04:00	40.7555	-73.9833	B02512
4	6/1/2014 0:07:00	40.6880	-74.1831	B02512
...	...	...	...	...
663839	6/30/2014 22:40:00	40.7332	-73.9872	B02764
663840	6/30/2014 23:12:00	40.7905	-73.9796	B02764
663841	6/30/2014 23:13:00	40.7640	-73.9887	B02764
663842	6/30/2014 23:15:00	40.7262	-73.9944	B02764
663843	6/30/2014 23:35:00	40.7404	-73.9848	B02764

	Date/Time	Lat	Lon	Base
0	5/1/2014 0:02:00	40.7521	-73.9914	B02512
1	5/1/2014 0:06:00	40.6965	-73.9715	B02512
2	5/1/2014 0:15:00	40.7464	-73.9838	B02512
3	5/1/2014 0:17:00	40.7463	-74.0011	B02512
4	5/1/2014 0:17:00	40.7594	-73.9734	B02512
...	...	...	...	...
652430	5/31/2014 23:45:00	40.7309	-74.0014	B02764
652431	5/31/2014 23:52:00	40.7528	-73.9798	B02764
652432	5/31/2014 23:55:00	40.7158	-73.9519	B02764
652433	5/31/2014 23:56:00	40.6961	-73.8997	B02764
652434	5/31/2014 23:59:00	40.7393	-73.9919	B02764

	Date/Time	Lat	Lon	Base
0	9/1/2014 0:01:00	40.2201	-74.0021	B02512
1	9/1/2014 0:01:00	40.7500	-74.0027	B02512
2	9/1/2014 0:03:00	40.7559	-73.9864	B02512
3	9/1/2014 0:06:00	40.7450	-73.9889	B02512
4	9/1/2014 0:11:00	40.8145	-73.9444	B02512
...	...	...	...	...
1028131	9/30/2014 22:57:00	40.7668	-73.9845	B02764
1028132	9/30/2014 22:57:00	40.6911	-74.1773	B02764
1028133	9/30/2014 22:58:00	40.8519	-73.9319	B02764
1028134	9/30/2014 22:58:00	40.7081	-74.0066	B02764
1028135	9/30/2014 22:58:00	40.7140	-73.9496	B02764

	Dispatching_base_num	Pickup_date	Affiliated_base_num	locationID
14270469	B02765	2015-05-08 15:42:00	B02764	79
14270470	B02765	2015-05-08 15:42:00	B02765	37
14270471	B02765	2015-05-08 15:42:00	B02765	161
14270472	B02765	2015-05-08 15:42:00	B02765	7
14270473	B02765	2015-05-08 15:43:00	B02711	25
14270474	B02765	2015-05-08 15:43:00	B02765	186
14270475	B02765	2015-05-08 15:43:00	B02765	263
14270476	B02765	2015-05-08 15:43:00	B02765	90
14270477	B02765	2015-05-08 15:44:00	B01899	45
14270478	B02765	2015-05-08 15:44:00	B02682	144

	locationID
count	1.427048e+07
mean	1.520574e+02
std	7.159620e+01
min	1.000000e+00
25%	9.200000e+01
50%	1.570000e+02
75%	2.300000e+02
max	2.650000e+02

	dispatching_base_number	date	active_vehicles	trips	year	month	day
0	B02512	2015-01-01	190	1132	2015	January	Thursday
1	B02765	2015-01-01	225	1765	2015	January	Thursday
2	B02764	2015-01-01	3427	29421	2015	January	Thursday
3	B02682	2015-01-01	945	7679	2015	January	Thursday
4	B02617	2015-01-01	1228	9537	2015	January	Thursday

	DATE	TIME	PICK UP ADDRESS	Unnamed: 3	Unnamed: 4	Unnamed: 5	year	month	day
0	2014-07-01	12:00:00 AM	874 E 139th St Mott Haven, BX	NaN	NaN	NaN	2014	July	Tuesday
1	2014-07-01	12:01:00 AM	628 E 141st St Mott Haven, BX	NaN	NaN	NaN	2014	July	Tuesday
2	2014-07-01	12:01:00 AM	601 E 156th St South Bronx, BX	NaN	NaN	NaN	2014	July	Tuesday
3	2014-07-01	12:01:00 AM	708 E 138th St Mott Haven, BX	NaN	NaN	NaN	2014	July	Tuesday
4	2014-07-01	12:02:00 AM	700 E 140th St Mott Haven, BX	NaN	NaN	NaN	2014	July	Tuesday

	Base Name	Total Number of Trips
0	12TH STREET LUXURY CARS, INC.	394
1	1431 CAR SERVICE, INC.	7642
2	204 CAR & LIMO INC	446
3	49 WESTCHESTER SQUARE PRESTIGE CAR SERVICE, CORP.	36199
4	510 CAR SERVICE CORP	87380
...	...	...
324	WC TRANSPORTATION SERVICE GROUP INC.	281
325	WELLCARE CAR SERVICE INC.	44
326	XPRESS TRANSPORT & MULTI SERVICE INC.	115
327	XYZ TWO WAY RADIO	138693
328	YELLOWSTONE TRANSPORTATION INC.	177179

	Year-Month	Total Monthly Trips
0	2015-01	977191
1	2015-02	957125
2	2015-03	823005
3	2015-04	1199269
4	2015-05	1149425
5	2015-06	941975
6	2015-07	474131
7	2015-08	28715

	Base Name	Month	Number of Trips
474	LA MEXICANA EXPRESS INC.	2015-06	87434
320	FAST OPERATING CORP	2015-05	79410
319	FAST OPERATING CORP	2015-04	77380
318	FAST OPERATING CORP	2015-03	75860
325	FENIX CAR SVCE CORP	2015-01	72926
...	...	...	...
351	GEMINI GLOBAL TRANSPORTATION,LLC.	2015-03	2
726	R TRANSPORT INC	2015-01	2
60	AMERICANA TRANSPORTATION LLC	2015-06	1
547	MICHAELS CAR SERVICE INC.	2015-01	1
649	OLS LIMOUSINE SERVICE INC.	2015-07	1

	time_of_trip	start_lat	start_lng	Unnamed: 3
5109	2014-09-27 02:34:00	40.68379	-73.97592	B02598
5110	2014-09-14 06:08:00	40.66873	-73.93280	B02598
5111	2014-09-10 01:47:00	40.68823	-73.97875	B02598
5112	2014-09-25 13:13:00	40.73263	-73.97433	B02598
5113	2014-09-11 07:25:00	40.71908	-73.86452	B02598
...	...	...	...	...
267687	2014-09-15 01:14:00	40.77385	-73.87396	b02764
267688	2014-09-17 12:34:00	40.73615	-73.99337	b02764
267689	2014-08-31 20:35:00	40.77116	-73.91735	b02764
267690	2014-09-07 13:37:00	40.72807	-73.98468	b02764
267691	2014-09-18 02:28:00	40.71909	-73.99006	b02764

	Date/Time	Lat	Lon	Base	year	month	day
0	2014-04-01 00:11:00	40.7690	-73.9549	B02512	2014	April	Tuesday
1	2014-04-01 00:17:00	40.7267	-74.0345	B02512	2014	April	Tuesday
2	2014-04-01 00:21:00	40.7316	-73.9873	B02512	2014	April	Tuesday
3	2014-04-01 00:28:00	40.7588	-73.9776	B02512	2014	April	Tuesday
4	2014-04-01 00:33:00	40.7594	-73.9722	B02512	2014	April	Tuesday

	Date/Time	Lat	Lon	Base	year	month	day
0	2014-08-01 00:03:00	40.7366	-73.9906	B02512	2014	August	Friday
1	2014-08-01 00:09:00	40.7260	-73.9918	B02512	2014	August	Friday
2	2014-08-01 00:12:00	40.7209	-74.0507	B02512	2014	August	Friday
3	2014-08-01 00:12:00	40.7387	-73.9856	B02512	2014	August	Friday
4	2014-08-01 00:12:00	40.7323	-74.0077	B02512	2014	August	Friday

	Date/Time	Lat	Lon	Base	year	month	day
0	2014-06-01 00:00:00	40.7293	-73.9920	B02512	2014	June	Sunday
1	2014-06-01 00:01:00	40.7131	-74.0097	B02512	2014	June	Sunday
2	2014-06-01 00:04:00	40.3461	-74.6610	B02512	2014	June	Sunday
3	2014-06-01 00:04:00	40.7555	-73.9833	B02512	2014	June	Sunday
4	2014-06-01 00:07:00	40.6880	-74.1831	B02512	2014	June	Sunday

	Date/Time	Lat	Lon	Base	year	month	day
0	2014-05-01 00:02:00	40.7521	-73.9914	B02512	2014	May	Thursday
1	2014-05-01 00:06:00	40.6965	-73.9715	B02512	2014	May	Thursday
2	2014-05-01 00:15:00	40.7464	-73.9838	B02512	2014	May	Thursday
3	2014-05-01 00:17:00	40.7463	-74.0011	B02512	2014	May	Thursday
4	2014-05-01 00:17:00	40.7594	-73.9734	B02512	2014	May	Thursday

	Date/Time	Lat	Lon	Base	year	month	day
0	2014-09-01 00:01:00	40.2201	-74.0021	B02512	2014	September	Monday
1	2014-09-01 00:01:00	40.7500	-74.0027	B02512	2014	September	Monday
2	2014-09-01 00:03:00	40.7559	-73.9864	B02512	2014	September	Monday
3	2014-09-01 00:06:00	40.7450	-73.9889	B02512	2014	September	Monday
4	2014-09-01 00:11:00	40.8145	-73.9444	B02512	2014	September	Monday

	Date
0	2014-07-01 00:00:00
1	2014-07-02 00:00:00
2	2014-07-03 00:00:00
3	2014-07-04 00:00:00
4	2014-07-05 00:00:00
...	...
2744870	2015-02-20 19:52:37
2744871	2015-02-20 17:28:27
2744872	2015-02-20 00:34:53
2744873	2015-02-20 13:26:20
2744874	2015-02-20 14:08:07

	Base
0	B00256
1	B01868
2	B01743
3	B02026
4	B01639
...	...
511	B01916
512	B02773
513	B02829
514	B02845
515	B02385

	Count
Date
2014-04-01 00:00:00	138
2014-04-01 01:00:00	66
2014-04-01 02:00:00	53
2014-04-01 03:00:00	93
2014-04-01 04:00:00	166

day	Friday	Monday	Saturday	Sunday	Thursday	Tuesday	Wednesday
month
April	315002	238429	324545	273560	372522	250632	338015
February	360136	264693	354962	284432	323955	276956	276172
January	325261	182785	369693	219884	316857	188802	235869
June	371225	375312	399377	334434	357782	405500	328141
March	309631	269931	314785	313865	277026	320634	256767
May	430134	255501	464298	390391	337607	290004	316045

	day	hours	size
0	Friday	0	78936
1	Friday	1	44064
2	Friday	2	26942
3	Friday	3	18845
4	Friday	4	22798
...	...	...	...
163	Wednesday	19	129724
164	Wednesday	20	122022
165	Wednesday	21	119511
166	Wednesday	22	113909
167	Wednesday	23	90740

	Date/Time	Lat	Lon	Base
4534322	9/30/2014 22:57:00	40.7668	-73.9845	B02764
4534323	9/30/2014 22:57:00	40.6911	-74.1773	B02764
4534324	9/30/2014 22:58:00	40.8519	-73.9319	B02764
4534325	9/30/2014 22:58:00	40.7081	-74.0066	B02764
4534326	9/30/2014 22:58:00	40.7140	-73.9496	B02764

	Lat	Lon	size
0	39.6569	-74.2258	1
1	39.6686	-74.1607	1
2	39.7214	-74.2446	1
3	39.8416	-74.1512	1
4	39.9055	-74.0791	1
...	...	...	...
574553	41.3730	-72.9237	1
574554	41.3737	-73.7988	1
574555	41.5016	-72.8987	1
574556	41.5276	-72.7734	1
574557	42.1166	-72.0666	1